Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization

Understand

The out-of-distribution (OOD) problem generally arises when neural networks encounter data that significantly deviates from the training data distribution, i.e., in-distribution (InD).

• In this paper, we study the OOD problem from a neuron activation view.
• We first formulate neuron activation states by considering both the neuron output and its influence on model decisions.
• Then, to characterize the relationship between neurons and OOD issues, we introduce the \textit{neuron activation coverage} (NAC) -- a simple measure for neuron behaviors under InD data.